Privacy Considerations Research Articles

FHG-PR: a hybridized fuzzy-AHP and game theory model for assessing privacy risk on social media platforms

Social media platforms have become integral to our daily lives, enabling global connectivity and interaction. Current research provides general solutions for mitigating privacy risks, but it does not focus on particular platforms. However, the voluntary sharing of personal information on these platforms presents considerable privacy risks, highlighting the need for effective risk assessment mechanisms. This study presents a hybrid approach that uses the fuzzy Analytical Hierarchy Process (AHP) and game theory to estimate privacy risks on Meta and X. We used a fuzzy AHP to rank determinant variables based on surveys of social media users and professionals. These weighted criteria were then used in a Multi-Criteria Decision-Making (MCDM) framework using cooperative game theory to discover ways to lower privacy hazards. The model accurately analyzed the privacy threats on Meta and X, recommending alternate techniques for preventing breaches in data privacy. The cooperative game theory method allows stakeholders to collaborate on creating privacy-preserving measures. The findings emphasize the model’s ability to address platform-specific privacy threats, as well as its flexibility in other social media settings. The model’s potential for real-world application is demonstrated by its ability to provide practical risk reduction measures, which improve privacy protection for social media users.

Teledentistry Integration: Revolutionizing Dental Insurance Through Technological Innovation

This article examines the transformative impact of teledentistry on the dental insurance industry, exploring how telecommunications technology is revolutionizing dental care delivery, access, and insurance. It discusses the expansion of dental care accessibility, streamlining of insurance processes, enhancement of preventive care, and the generation of data-driven insights through teledentistry. The article also addresses privacy and security considerations, presents case studies of successful implementations, and explores future prospects and innovations in the field. By analyzing the integration of teledentistry with artificial intelligence and wearable technology, the article highlights the potential for more proactive, personalized, and accessible dental care, while emphasizing the need for continued research and development to fully realize these benefits.

Machine Learning Meets Cybersecurity

In an era where digital transformation is accelerating, the integration of machine learning (ML) into cybersecurity is becoming increasingly crucial. This article delves into the significant role that ML plays in enhancing cybersecurity measures. ML's ability to process and analyze vast amounts of data at unprecedented speeds allows for more efficient threat detection, anomaly identification, and predictive analytics. These capabilities are essential in pre-empting and mitigating cyber threats in real-time. However, the same technologies that bolster defences are also being leveraged by cybercriminals to develop more sophisticated attacks, posing unique challenges. This article explores the dual-edged nature of ML in cybersecurity, highlighting both its benefits and the potential risks. It discusses the challenges of bias in ML algorithms, the vulnerabilities exposed through adversarial attacks, and the ethical and privacy considerations that come with deploying ML solutions. Furthermore, it provides insights into the future trends and developments in ML-driven cybersecurity and underscores the importance of synergizing human expertise with machine intelligence to build more resilient cybersecurity frameworks. Through this exploration, the article aims to provide a comprehensive understanding of the transformative impact of ML on cybersecurity and the path forward in addressing the accompanying challenges.

Quantum-assisted federated intelligent diagnosis algorithm with variational training supported by 5G networks

In the realm of intelligent healthcare, there is a growing ambition to reshape medical services through the integration of artificial intelligence (AI). However, conventional machine learning faces inherent challenges such as privacy issues, delayed updates, and protracted training times, particularly due to the hesitance of medical institutions to directly share sensitive data, with possible noises. In response to these concerns, a Quantum-Assisted Federated Intelligent Diagnosis Algorithm (β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document}-QuAFIDA) is proposed, applied into real medical data. Leveraging the capabilities of the 5G mobile network, this approach works the connection between Internet of Medical Things (IoMT) devices through the 5G, synchronizing training and updating the server model without disrupting their real-world applications. In our quest to safeguard patient data and enhance training efficiency, our study employs an innovative heuristic approach marked by a nested loop structure. Specifically, the inner loop is dedicated to training the beta-variational quantum eigensolver (β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document}-VQE) to approximate the expectation values of the proposed algorithm; the outer loop trains the β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document}-QuAFIDA to reduce the relative entropy towards the target. This approach involves a balance between privacy considerations and the urgency of training. Results demonstrate that representations with low-rank attained through β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document}-QuAFIDA offer an effective approach for acquiring low-rank states. This research signifies a step forward in the synergy between AI and 5G technologies, presenting a novel avenue for the advancement of intelligent healthcare.

An autoencoder-based confederated clustering leveraging a robust model fusion strategy for federated unsupervised learning

Concerns related to data privacy, security, and ethical considerations become more prominent as data volumes continue to grow. In contrast to centralized setups, where all data is accessible at a single location, model-based clustering approaches can be successfully employed in federated settings. However, this approach to clustering in federated settings is still relatively unexplored and requires further attention. As federated clustering deals with remote data and requires privacy and security to be maintained, it poses particular challenges as well as possibilities. While model-based clustering offers promise in federated environments, a robust model aggregation method is essential for clustering rather than the generic model aggregation method like Federated Averaging (FedAvg). In this research, we proposed an autoencoder-based clustering method by introducing a novel model aggregation method FednadamN, which is a fusion of Adam and Nadam optimization approaches in a federated learning setting. Therefore, the proposed FednadamN adopted the adaptive learning rates based on the first and second moments of gradients from Adam which offered fast convergence and robustness to noisy data. Furthermore, FednadamN also incorporated the Nesterov-accelerated gradients from Nadam to further enhance the convergence speed and stability. We have studied the performance of the proposed Autoencoder-based clustering methods on benchmark datasets and using the novel FednadamN model aggregation strategy. It shows remarkable performance gain in federated clustering in comparison to the state-of-the-art.

Edge Computing and Analytics for IoT Devices: Enhancing Real-Time Decision Making in Smart Environments

This article presents a comprehensive analysis of edge computing and analytics for Internet of Things (IoT) devices, addressing the growing need for real-time processing and reduced latency in IoT applications. We explore the evolution of IoT architectures, from cloud-centric models to edge-enabled systems, and examine the key components and data flows in edge computing environments. Through a detailed comparison of cloud and edge processing, we demonstrate significant latency reductions across various IoT scenarios, with improvements from hundreds of milliseconds to mere milliseconds. The article delves into crucial aspects of data management in edge computing, including local versus cloud processing trade-offs, data synchronization strategies, and privacy considerations. A case study of an edge analytics pipeline in a smart factory setting showcases practical implementations, revealing substantial improvements in anomaly detection speed, bandwidth utilization, and overall system efficiency. The case study achieved a 92.5% reduction in anomaly detection latency and an 85% decrease in bandwidth usage. Finally, we discuss ongoing challenges and future directions in edge computing, including scalability issues, standardization efforts, and the integration of emerging technologies such as 5G and AI accelerators. This article contributes to the growing body of knowledge on edge computing in IoT, offering insights into its transformative potential for creating more responsive and intelligent systems across diverse applications.

Security and Privacy Considerations in Voice Assistant Data Entry Systems for Healthcare Providers

Security and Privacy Considerations in Voice Assistant Data Entry Systems for Healthcare Providers

Data-Driven Personalization : Revolutionizing User Experience

Data-driven personalization has emerged as a transformative approach in digital user experience design, leveraging advanced analytics and machine learning to tailor content and interfaces to individual users. This article explores five key aspects of data-driven personalization: user behavior analysis, segmentation and targeting, machine learning algorithms, real-time adaptation, and privacy and ethical considerations. It examines the significant impact of personalization on business outcomes, including increased revenue and customer engagement, while also addressing implementation challenges, such as technological complexity and privacy concerns. The article provides insights into the methodologies, processes, and best practices for effective personalization, supported by industry statistics and case studies, offering a comprehensive overview of how organizations can harness this powerful approach to create more engaging, relevant, and effective digital experiences.

Advancing athlete safety through real-time ECG monitoring for enhanced cardiovascular health in sports performance

This research paper explores the implementation and efficacy of real-time electrocardiogram (ECG) monitoring systems for athletes, emphasizing their potential to significantly enhance safety and performance in sports settings. By utilizing advanced ECG technology, the study investigates how continuous, real-time heart rate and rhythm monitoring can aid in the immediate detection of cardiovascular anomalies during high-intensity activities. The research methodology incorporates the deployment of portable ECG devices in a controlled experimental setup, analyzing data from athletes during training sessions and competitive events. Results from the study highlight the system's ability to provide swift and accurate cardiac assessments, thereby enabling timely medical interventions. Moreover, the paper discusses the technical challenges associated with real-time ECG monitoring, such as signal interference and data accuracy, and addresses privacy and ethical considerations concerning the continuous collection of health data. The discussion extends to the implications of integrating such technology within sports medicine, suggesting that while the systems offer substantial benefits in monitoring and preventing cardiac issues, they also necessitate rigorous standards for data security and ethical oversight. The conclusion advocates for a balanced approach to the adoption of these technologies, proposing future research directions that focus on enhancing system reliability and integrating artificial intelligence to predict potential health risks proactively. This study contributes to the ongoing discourse in sports health technology by providing a comprehensive analysis of real-time ECG monitoring as a transformative tool for athlete healthcare management.

Next-generation fall detection: harnessing human pose estimation and transformer technology

ABSTRACT Elderly falls are occurring at an alarming rate, with significant health risks for seniors. Current fall detection systems often lack accuracy, efficacy, and privacy considerations. This study examines three leading human pose estimation frameworks combined with transformer deep learning models to develop a lightweight, privacy-preserving fall detection system. Key features include: 1) It runs on low-power devices like Raspberry Pis; 2) It monitors seniors passively, without requiring active participation; 3) It can be deployed in any residential or senior care setting; 4) It does not rely on wearables; and 5) All processing occurs locally, ensuring privacy with only fall alerts transmitted to caregivers. In real-world tests, the model achieved 95.24% sensitivity, 89.80% specificity, 98.00% accuracy, a 90.91% F1 score, and 95.24% precision, highlighting its effectiveness in detecting falls among the elderly while maintaining privacy and security.

Survey on haptic technologies for virtual reality applications during COVID-19

<p>This paper presents a comprehensive survey on the advancements and applications of haptic technologies, which are methods that facilitate the sense of touch and movement, in virtual reality (VR) during the COVID-19 pandemic. It aims to identify and classify the various domains in which haptic technologies have been utilized or can be adapted to combat the unique challenges posed by the pandemic or public health emergencies in general. Existing reviews and surveys that concentrate on the applications of haptic technologies during the Covid-19 pandemic are often limited to specific domains; this survey strives to identify and consolidate all application domains discussed in the literature, including healthcare, medical training, education, social communication, and fashion and retail. Original research and review articles were collected from the Web of Science Core Collection as the main source, using a combination of keywords (like ‘haptic’, ‘haptics’, ‘touch interface’, ‘tactile’, ‘virtual reality’, ‘augmented reality’, 'Covid-19', and ‘pandemic’) and Boolean operators to refine the search and yield relevant results. The paper reviews various haptic devices and systems and discusses the technological advancements that have been made to offer more realistic and immersive VR experiences. It also addresses challenges in haptic technology in VR, including fidelity, ethical, and privacy considerations, and cost and accessibility issues.</p>

Blockchain adoption factors in agricultural supply chains: A PLS-SEM study

The purpose of this study is to explore factors influencing the blockchain adoption in agricultural supply chains, to make a particular focus on how security and privacy considerations, policy support, and management support impact the blockchain adoption intention. it further investigates perceived usefulness as a mediating variable that potentially amplifies the effects of these factors on blockchain adoption intention, and sets perceived cost as a moderating variable to test its influence on the strength and direction of the relationship between perceived usefulness and adoption intention. through embedding the cost-benefit theory into the integrated tam-toe framework and utilizing the partial least squares structural equation modeling (PLS-SEM) method, this study identifies the pivotal factors that drive or impede blockchain adoption in the agricultural supply chains, which fills the gap of the relatively insufficient research on the blockchain adoption in agriculture field. the results further provide empirical evidence and strategic insights that can guide practical implementations, to equip stakeholders or practitioners with the necessary knowledge to navigate the complexities of integrating cutting-edge technologies into traditional agricultural operations, thereby promoting more efficient, transparent, and resilient agricultural supply chains.

The Impact and Prospects of AI-Generated Content in Educational Environments

This paper examines the impact and emerging prospects of generative artificial intelligence (AI) content creation (AIGC) within the educational sector. As AI technology continues to evolve rapidly, the potential for AIGC to revolutionize educational practices has garnered significant interest and scholarly focus. AIGC is proving instrumental in developing customized educational content, facilitating personalized learning experiences, and enhancing assessment methods. These capabilities have the potential to significantly boost teaching efficiency and elevate student outcomes. Nonetheless, the integration of AIGC also poses several challenges, including concerns over academic integrity, shifts in the instructional roles of educators, and issues related to data privacy and ethical considerations. This analysis delves into the current applications of AIGC in education, discussing technological trends, innovations in educational models, and strategic policy recommendations. It underscores the necessity for future research to concentrate on evaluating the practical effects of AIGC, addressing potential drawbacks, and ensuring its responsible growth in educational settings.

Web Scrapping Tools used in Healthcare Sector

Web scraping has become essential in the healthcare sector and provides vast opportunities for data access and analysis. This abstract outlines several key web scraping tools commonly used in healthcare:: Scrapy: A Python-based framework for structured data extraction from various medical sources, offering adaptability and scalability. Beautiful Soup: Facilitates seamless data extraction from medical websites and research articles with its user-friendly interface. Selenium: Particularly useful for extracting data from dynamic healthcare websites driven by JavaScript. Octoparse: Empowers healthcare professionals to extract data without programming expertise through an intuitive interface. ParseHub: Simplifies data extraction from complex healthcare websites with scheduling capabilities. Import.io: A visual API-based scraping solution for integrating and analyzing data from web pages and APIs. WebHarvy: Streamlines data retrieval from medical websites, journals, and forums with automatic configuration and diverse export options. Web scraping tools are invaluable for accessing and analyzing health information, enhancing assessment, decision-making, and patient outcomes. However, adherence to legal and ethical guidelines, including data privacy and consent considerations, is crucial for responsible use in the healthcare industry.

The use of electronic evidence in the criminal process of foreign countries

It is emphasized that electronic evidence has become indispensable for criminal investigations and trials around the world. The importance of legal regulation of the criminal procedural form of digital technologies is emphasized. The improper nature of such regulation entails certain legal consequences, including the recognition of electronic evidence as improper or inadmissible. The experience of some foreign countries in the field of recognition and use of electronic evidence (the USA, Great Britain, the People’s Republic of China and the countries of the European Union) is analyzed. It has been proven that each state builds its own way and approach to incorporating such evidence into its legal systems depending on legal traditions, technological infrastructure and privacy considerations. From the analysis of procedural codes, it was noted that procedural science develops a theory of electronic evidence according to the industry principle. It is emphasized that they are based on common digital technologies, therefore this provides an opportunity to develop uniform cross­industry rules for the use of electronic evidence in any process. It was determined that in the Anglo-Saxon legal system there is no clear division of evidence into types. Evidence law is based on typical problem situations. The US legislation, which regulates the use of electronic evidence in the criminal process, is analyzed. It is noted that the condition for their acceptance is its authenticity and relevance to the case. It is noted that in the UK, all electronic evidence is subject to the same rules and laws that apply to documentary evidence. The legislation of the European Union, which regulates electronic evidence and allows to strengthen cooperation between member states in the field of using electronic evidence, is analyzed. It is emphasized that as states continue to adapt to the age of digital technologies, the development of a reliable legal framework and international cooperation will be essential to ensure the effective and fair use of electronic evidence in the pursuit of justice.

Determining the Acceptability of Targeted Apps for High-Risk Alcohol Consumption in Nurses: A Qualitative Study

Aim To determine the acceptability of targeted apps and provide recommendations for the implementation of and app addressing high risk alcohol use to nurses. Design A qualitative descriptive study design, using the Behavioural Change Wheel implementation framework. Methods Semi-structured interviews with 42 Australian nurses were subject to structural coding using the Capability, Opportunity, and Motivation (COM-B) model linked to the Behaviour Change Wheel. Qualitative data has been reported using the COREQ framework. Results Most participants agreed that targeted apps would appeal to nurses, provided specific design considerations were included. These considerations related to privacy and confidentiality, strategies to target the app to nurses across wide age and experience ranges and identified the need for a considered campaign to both launch the app and position it with existing interventions for high-risk alcohol use. Conclusions Our findings indicate that a targeted app to reduce high-risk alcohol consumption could be acceptable to nurses, however the needs to include specific components suitable for nurses. We recommend further research into specific components of a targeted app, leading to a co-design process where nurses can determine app components and function. Summary of relevance High-risk alcohol consumption has been shown to be an issue amongst nurses. Targeted apps have been shown to have an effect in addressing high-risk alcohol consumption among specific groups. However, consideration for privacy of data provided to the app must be considered, especially given the link between disciplinary action, loss of role identity, and nurse suicide. This paper indicates that nurses would accept a targeted app, subject to specific design considerations, particularly related to confidentiality.

From shallows to depths: unveiling hybrid synthetic data modeling for enhanced learning with privacy considerations in naturally imbalanced datasets

Data serves as the foundational element that drives model development and performance in machine learning and deep learning. The learning algorithms are as good as data on which they are trained on. Data constrained environments pose serious threat to the effectiveness of learning algorithms. Data limitations stem from a range of factors, including regulatory restrictions, privacy concerns, and the inherent scarcity of relevant data. This constrained availability of data often leads to class imbalance problem in the context of classification tasks. To address the challenges of limited data, the algorithmic generated data, also known as synthetic data, is gaining significant traction as a cost-effective, readily available, and secure alternative. Synthetic data generation techniques can be employed to enhance dataset size by augmenting data samples and to address class imbalance by increasing the number of minority class instances. These techniques generally fall into two main categories: distance-based shallow models and probability estimation-based deep generative models. Shallow interpolation-based models generate new data points within the local space between existing data points, while deep density estimation based models generate new data by learning the whole distribution of data. . In the context of smaller datasets, deep generative models often struggle to accurately estimate the probability distribution of whole data. To effectively represent the global data distribution, these deep models require initial starting data samples to guide their approximation. This paper examines the potential of integration of shallow and deep generative models in the data generation pipeline for effective synthetic data augmentation which furthers enhanced learning and generalization of downstream tasks. In this work, we present a hybrid approach of tabular data generation involving mixed type data attributes (continuous, discrete) and pay special attention to data imbalance and insufficient data problems. We introduce the Hybrid Data Balancing and Augmentation Approach for Mixed Tabular Data (HDBA-MTD), specifically designed to synthesize samples for underrepresented labels of output class and address issues of insufficient data instances. This approach enhances training data diversity, thereby paying special attention to the downstream classification and generalization performance. Experiments are carried out using benchmark datasets to assess the practicality of the presented hybrid model in real-world scenarios. This work has also attempted to quantify the privacy preservability for real data concerning ethical considerations and data security circumstances. The evaluation and analysis of these experiments show that the present hybrid model performs favorably compared to other current hybrid synthetic data generation methods.

Pseudo-label refinement via hierarchical contrastive learning for source-free unsupervised domain adaptation

Source-free unsupervised domain adaptation aims to adapt a source model to an unlabeled target domain without accessing the source data due to privacy considerations. Existing works mainly solve the problem by self-training methods and representation learning. However, these works typically learn the representation on a single semantic level and barely exploit the rich hierarchical semantic information to obtain clear decision boundaries, which makes it hard for these methods to achieve satisfactory generalization performance. In this paper, we propose a novel hierarchical contrastive domain adaptation algorithm that exploits self-supervised contrastive learning on both fine-grained instances and coarse-grained cluster semantics. On the one hand, we propose an adaptive prototype pseudo-labeling strategy to obtain much more reliable labels. On the other hand, we propose hierarchical contrastive representation learning on both fine-grained instance-wise level and coarse-grained cluster level to reduce the negative effect of label noise and stabilize the whole training procedure. Extensive experiments are conducted on primary unsupervised domain adaptation benchmark datasets, and the results demonstrate the effectiveness of the proposed method.

Practical and Robust Federated Learning With Highly Scalable Regression Training.

Privacy-preserving federated learning, as one of the privacy-preserving computation techniques, is a promising distributed and privacy-preserving machine learning (ML) approach for Internet of Medical Things (IoMT), due to its ability to train a regression model without collecting raw data of data owners (DOs). However, traditional interactive federated regression training (IFRT) schemes rely on multiple rounds of communication to train a global model and are still under various privacy and security threats. To overcome these problems, several noninteractive federated regression training (NFRT) schemes have been proposed and applied in a variety of scenarios. However, there are still several challenges: 1) how to protect the privacy of DOs' local dataset; 2) how to realize highly scalable regression training without linear dependence on sample dimension; 3) how to tolerate DOs' dropout; and 4) how to enable DOs to verify the correctness of aggregated results returned from the cloud service provider (CSP). In this article, we propose two practical noninteractive federated learning schemes with privacy-preserving for IoMT, named homomorphic encryption based NFRT (HE-NFRT) and double-masking protocol based NFRT (Mask-NFRT), respectively, which are based on a comprehensive consideration of NFRT, privacy concerns, high-efficiency, robustness, and verification mechanism. The security analyses display that our proposed schemes are able to protect the privacy of DOs' local training data, resist collusion attack, and support strong verification to each DO. The performance evaluation results demonstrate that our proposed HE-NFRT scheme is desirable for a high-dimensional and high-security IoMT application while Mask-NFRT scheme is desirable for a high-dimensional and large-scale IoMT application.

Assessing the Recreational Fishers and their Catches based on Social Media Platforms: Privacy and Ethical Data Analysis Considerations

Social Media (SM) can offer insights to track recreational Fishing (Rf); Many Limitations Hinder Its Practical Application. The proportion and characteristics of RFs that share their catches remain unidentified. The objective was to enhance the surveillance abilities of RF by utilizing SM information. This study focuses on the sharing economy information transmission, network optimization of SM platforms, and privacy protection issues during data transmission. The study starts with the data transmission characteristics in SM platforms, analyzes the data ethics in SM information transmission from the perspectives of natural and economic data attributes, and then focuses on the privacy protection principles in network information transmission. Then, a privacy protection scheme based on a locally sensitive hash algorithm is constructed, and finally, an information transmission scheme and method optimization based on a network optimization module are proposed. The research gathered data using physical (face-to-face) surveys and digital (email) surveys to define marine RF that post catches on online platforms (“sharers”), together with additional demographic and fishing data. A comparative analysis was conducted on the computational convergence and accuracy of different privacy protection methods, and the optimal rule framework for data privacy protection was discussed. The observation results show that the efficiency of information transmission using the Minhash-SSNR method is slightly inferior to that using the OSNR-SSNR method. Minhash technology is based on a similarity comparison of dimensionality-reduced data. This leads to data causing the initially highly similar two sets of lists to be misjudged as not having enough similarity, thereby reducing some potential information dissemination opportunities and affecting the efficacy of information transmission. It can be seen that the Minhash-SSNR strategy can effectively send information to nodes with high similarity, preventing excessive information duplication within the system. Although the Minhash-SSNR strategy has a certain degree of decline in information transmission efficiency, it accounts for only about 5% compared to the OSNR-SSNR strategy, ensuring the essential operational stability of SM opportunity networks without significant impact. With few learning and training times, the network information transmission optimization module proposed in this study quickly achieved a lower exponential error. As the number of training sessions gradually increases, the exponential error of the network information transmission optimization module in this study is small, and the prediction accuracy of the method is high. RFs who captured a prize, iconic, or symbolic species tended to discuss their catches more. This research signifies significant progress in incorporating SM information to oversee RFs.